用Kalman滤波模型评价海岸线预测精度——以孟加拉湾Nijhum Dwip为例

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-01-01 DOI:10.1016/j.rsase.2025.101469

Anamika Das Kona , Md Enamul Hoque , Md Atiqur Rahman

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引用次数: 0

摘要

岸线动力学在海岸带管理和环境保护中起着重要作用。本研究调查了位于梅克纳河河口的Nijhum Dwip在1980年至2020年期间的海岸线变化和预测，并对2030年进行了预测。利用多时相陆地卫星影像、数字海岸线分析系统（DSAS）和卡尔曼滤波模型，分析了海岸线的时空变化。结果表明，在B段，岸线净移动量为1322.85 m，平均移动速率为31.96 m/年。A段为适度吸积，平均吸积速率为7.79 m/yr。卡尔曼滤波模型预测，到2030年，平均海平面上升1601.23米，与历史上的海平面上升模式一致。通过均方根误差（RMSE）分析的模型验证得出95米的值，突出了预测和观测到的海岸线位置之间的差异。这项综合研究强调了先进的地理空间和统计方法在沿海变化监测中的效用，并为可持续沿海管理提供了可行的见解。

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Evaluating shoreline prediction accuracy with the Kalman filter model: A case study of Nijhum Dwip, Bay of Bengal

Shoreline dynamics play a critical role in coastal zone management and environmental conservation. This study investigates shoreline changes and predictions for Nijhum Dwip, located in the Meghna estuary, over the period from 1980 to 2020, with a forecast for 2030. Utilizing multi-temporal Landsat imagery, Digital Shoreline Analysis System (DSAS), and the Kalman Filter Model, the study analyzes spatial and temporal shoreline variations. Results indicate a significant accretion trend, particularly in Segment B, which exhibits a net shoreline movement of 1322.85 m and an average rate of 31.96 m/yr. Segment A shows moderate accretion, with an average rate of 7.79 m/yr. The Kalman Filter Model predicts a mean accretion of 1601.23 m by 2030, aligning with historical accretion patterns. Model validation through Root Mean Square Error (RMSE) analysis yields a value of 95 m, highlighting discrepancies between predicted and observed shoreline positions. This comprehensive study underscores the utility of advanced geospatial and statistical methods in coastal change monitoring and provides actionable insights for sustainable coastal management.

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems